This function computes the confidence interval (CI) of the sensitivity and specificity of the thresholds given in argument. By default, the 95% CI are computed with 2000 stratified bootstrap replicates.
# ci.thresholds(...)
# S3 method for roc
ci.thresholds(roc, conf.level=0.95, boot.n=2000,
boot.stratified=TRUE, thresholds = "local maximas",
progress=getOption("pROCProgress")$name, parallel=FALSE, ...)
# S3 method for formula
ci.thresholds(formula, data, ...)
# S3 method for smooth.roc
ci.thresholds(smooth.roc, ...)
# S3 method for default
ci.thresholds(response, predictor, ...)A list of length 2 and class “ci.thresholds”, “ci” and “list” (in this order), with the confidence intervals of the CI and the following items:
a matrix of CI for the specificity. Row (names) are the thresholds, the first column the lower bound, the 2nd column the median and the 3rd column the upper bound.
same than specificity.
Additionally, the list has the following attributes:
the width of the CI, in fraction.
the number of bootstrap replicates.
whether or not the bootstrapping was stratified.
the thresholds, as given in argument.
the object of class “roc” that was used to compute the CI.
a “roc” object from the roc function.
not available for smoothed ROC curves, available only to catch the error and provide a clear error message.
arguments for the roc function.
a formula (and possibly a data object) of type
response~predictor for the roc function.
the width of the confidence interval as [0,1], never in percent. Default: 0.95, resulting in a 95% CI.
the number of bootstrap replicates. Default: 2000.
should the bootstrap be stratified (default, same number of cases/controls in each replicate than in the original sample) or not.
on which thresholds to evaluate the CI. Either the
numeric values of the thresholds, a logical vector (as index of
roc$thresholds) or a character “all”, “local
maximas” or “best” that will be used to determine the threshold(s)
on the supplied curve with coords (not on the resampled curves).
the name of progress bar to display. Typically
“none”, “win”, “tk” or “text” (see the
name argument to create_progress_bar for
more information), but a list as returned by create_progress_bar
is also accepted. See also the “Progress bars” section of
this package's documentation.
if TRUE, the bootstrap is processed in parallel, using parallel backend provided by plyr (foreach).
further arguments passed to or from other methods,
especially arguments for roc and ci.thresholds.roc
when calling ci.thresholds.default or ci.thresholds.formula.
Arguments for txtProgressBar (only
char and style) if applicable.
Arguments best.method and best.weights to coords.
If boot.stratified=FALSE and the sample has a large imbalance between
cases and controls, it could happen that one or more of the replicates
contains no case or control observation, producing a NA area.
The warning “NA value(s) produced during bootstrap were ignored.”
will be issued and the observation will be ignored. If you have a large
imbalance in your sample, it could be safer to keep
boot.stratified=TRUE.
ci.thresholds.formula and ci.thresholds.default are convenience methods
that build the ROC curve (with the roc function) before
calling ci.thresholds.roc. You can pass them arguments for both
roc and ci.thresholds.roc. Simply use ci.thresholds
that will dispatch to the correct method.
This function creates boot.n bootstrap replicate of the ROC
curve, and evaluates the sensitivity and specificity at thresholds
given by the thresholds argument. Then it computes the
confidence interval as the percentiles given by conf.level.
A threshold given as a logical vector or character is converted to the corresponding numeric vector once
using the supplied ROC curve, and not at each bootstrap iteration. See ci.coords for the latter behaviour.
For more details about the bootstrap, see the Bootstrap section in this package's documentation.
James Carpenter and John Bithell (2000) ``Bootstrap condence intervals: when, which, what? A practical guide for medical statisticians''. Statistics in Medicine 19, 1141--1164. DOI: tools:::Rd_expr_doi("10.1002/(SICI)1097-0258(20000515)19:9<1141::aid-sim479>3.0.CO;2-F").
Tom Fawcett (2006) ``An introduction to ROC analysis''. Pattern Recognition Letters 27, 861--874. DOI: tools:::Rd_expr_doi("10.1016/j.patrec.2005.10.010").
Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) ``pROC: an open-source package for R and S+ to analyze and compare ROC curves''. BMC Bioinformatics, 7, 77. DOI: tools:::Rd_expr_doi("10.1186/1471-2105-12-77").
Hadley Wickham (2011) ``The Split-Apply-Combine Strategy for Data Analysis''. Journal of Statistical Software, 40, 1--29. URL: tools:::Rd_expr_doi("10.18637/jss.v040.i01").
roc,
ci